An algorithm based on structural analysis for model-based fault diagnosis

Authors:
Esteban R. Gelso;Sandra M. Castillo;Joaquim Armengol
Affiliations:
IIiA --Universitat de Girona, Campus de Montilivi, Girona, E-17071, Spain, {esteban.gelso, sandra.castillo, joaquim.armengol}@udg.edu;IIiA --Universitat de Girona, Campus de Montilivi, Girona, E-17071, Spain, {esteban.gelso, sandra.castillo, joaquim.armengol}@udg.edu;IIiA --Universitat de Girona, Campus de Montilivi, Girona, E-17071, Spain, {esteban.gelso, sandra.castillo, joaquim.armengol}@udg.edu
Venue:
Proceedings of the 2008 conference on Artificial Intelligence Research and Development: Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence
Year:
2008

Citing 6
Cited 0

Readings in model-based diagnosis

Readings in model-based diagnosis
Diagnosis and Fault-Tolerant Control

Diagnosis and Fault-Tolerant Control
Conflicts versus analytical redundancy relations: a comparative analysis of the model based diagnosis approach from the artificial intelligence and automatic control perspectives

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Possible conflicts: a compilation technique for consistency-based diagnosis

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Diagnosability Analysis Based on Component-Supported Analytical Redundancy Relations

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
An Efficient Algorithm for Finding Minimal Overconstrained Subsystems for Model-Based Diagnosis

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

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Abstract

This paper presents a diagnosis system in which, an algorithm automatically finds all minimal structurally overdetermined (MSO) sets in a structural model of a system. It combines a first set of MSO sets to get the additional MSO sets, which were obtained after determining a complete matching between equations and unknown variables. Finding the complete set is useful for the diagnosis task increasing the fault isolability due to the fact that it can provide different signatures to each fault. The efficiency of the algorithm for finding all MSO sets is illustrated using the DAMADICS benchmark, which is a pneumatically-actuated control valve.